{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:21:32Z","timestamp":1787728892699,"version":"build-2784847793"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32100513"],"award-info":[{"award-number":["32100513"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32500560"],"award-info":[{"award-number":["32500560"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic and Applied Basic Research Foundation of Guangzhou, China","award":["SL2023A04J00291"],"award-info":[{"award-number":["SL2023A04J00291"]}]},{"name":"the Science and Technology Program of Guangzhou, China","award":["2024A04J3341"],"award-info":[{"award-number":["2024A04J3341"]}]},{"name":"Guangzhou Basic Research Program \u2013 Municipal\u2013Institute Joint Funding Project","award":["202524"],"award-info":[{"award-number":["202524"]}]},{"name":"the Postdoctoral Fellowship Program of CPSF","award":["GZC20230617"],"award-info":[{"award-number":["GZC20230617"]}]},{"DOI":"10.13039\/501100006407","name":"Henan Province Natural Science Foundation","doi-asserted-by":"crossref","award":["252300421598"],"award-info":[{"award-number":["252300421598"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Interdiscip Sci Comput Life Sci"],"DOI":"10.1007\/s12539-026-00869-w","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T06:39:41Z","timestamp":1787726381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations"],"prefix":"10.1007","author":[{"given":"Haipeng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douyue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minglei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0454-6935","authenticated-orcid":false,"given":"Wenliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"issue":"7","key":"869_CR1","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1080\/15476286.2020.1737441","volume":"17","author":"W Zhang","year":"2020","unstructured":"Zhang W, Yao G, Wang J et al (2020) ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs. RNA Biol 17(7):943\u2013955. https:\/\/doi.org\/10.1080\/15476286.2020.1737441","journal-title":"RNA Biol"},{"issue":"3","key":"869_CR2","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.cell.2018.01.011","volume":"172","author":"F Kopp","year":"2018","unstructured":"Kopp F, Mendell JT (2018) Functional classification and experimental dissection of long noncoding RNAs. Cell 172(3):393\u2013407. https:\/\/doi.org\/10.1016\/j.cell.2018.01.011","journal-title":"Cell"},{"issue":"1","key":"869_CR3","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1186\/s13046-025-03465-w","volume":"44","author":"L Zhao","year":"2025","unstructured":"Zhao L, Si K, Luo S et al (2025) Non-canonical activation of MAPK signaling by the lncRNA ASH1L-AS1-encoded microprotein APPLE through inhibition of PP1\/PP2A-mediated ERK1\/2 dephosphorylation in hepatocellular carcinoma. J Exp Clin Cancer Res 44(1):200. https:\/\/doi.org\/10.1186\/s13046-025-03465-w","journal-title":"J Exp Clin Cancer Res"},{"issue":"1","key":"869_CR4","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1186\/s13046-025-03546-w","volume":"44","author":"K Si","year":"2025","unstructured":"Si K, Zhang L, Jiang Z et al (2025) A novel lncRNA-mediated signaling axis governs cancer stemness and splicing reprogramming in hepatocellular carcinoma with therapeutic potential. J Exp Clin Cancer Res 44(1):287. https:\/\/doi.org\/10.1186\/s13046-025-03546-w","journal-title":"J Exp Clin Cancer Res"},{"issue":"11","key":"869_CR5","doi-asserted-by":"publisher","first-page":"166727","DOI":"10.1016\/j.jmb.2020.166727","volume":"433","author":"W Zhang","year":"2021","unstructured":"Zhang W, Zeng B, Yang M et al (2021) ncRNAVar: a manually curated database for identification of noncoding RNA variants associated with human diseases. J Mol Biol 433(11):166727. https:\/\/doi.org\/10.1016\/j.jmb.2020.166727","journal-title":"J Mol Biol"},{"issue":"D1","key":"869_CR6","doi-asserted-by":"publisher","first-page":"D192","DOI":"10.1093\/nar\/gkac981","volume":"51","author":"Y Yang","year":"2023","unstructured":"Yang Y, Wang D, Miao YR et al (2023) lncRNASNP v3: an updated database for functional variants in long non-coding RNAs. Nucleic Acids Res 51(D1):D192\u2013D198. https:\/\/doi.org\/10.1093\/nar\/gkac981","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"869_CR7","doi-asserted-by":"publisher","first-page":"e1812","DOI":"10.1002\/wrna.1812","volume":"14","author":"H Zhou","year":"2023","unstructured":"Zhou H, Hao X, Zhang P et al (2023) Noncoding RNA mutations in cancer. Wiley Interdiscip Rev RNA 14(6):e1812. https:\/\/doi.org\/10.1002\/wrna.1812","journal-title":"Wiley Interdiscip Rev RNA"},{"issue":"20","key":"869_CR8","doi-asserted-by":"publisher","first-page":"5432","DOI":"10.1158\/0008-5472.CAN-19-0773","volume":"79","author":"S Wu","year":"2019","unstructured":"Wu S, Sun H, Wang Y et al (2019) MALAT1 rs664589 polymorphism inhibits binding to miR-194-5p, contributing to colorectal cancer risk, growth, and metastasis. Cancer Res 79(20):5432\u20135441. https:\/\/doi.org\/10.1158\/0008-5472.CAN-19-0773","journal-title":"Cancer Res"},{"key":"869_CR9","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.omtn.2019.08.004","volume":"18","author":"Y Gao","year":"2019","unstructured":"Gao Y, Li X, Zhi H et al (2019) Comprehensive characterization of somatic mutations impacting lncRNA expression for pan-cancer. Mol Ther Nucleic Acids 18:66\u201379. https:\/\/doi.org\/10.1016\/j.omtn.2019.08.004","journal-title":"Mol Ther Nucleic Acids"},{"issue":"10","key":"869_CR10","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1038\/nm.4401","volume":"23","author":"DSW Tan","year":"2017","unstructured":"Tan DSW, Chong FT, Leong HS et al (2017) Long noncoding RNA EGFR-AS1 mediates epidermal growth factor receptor addiction and modulates treatment response in squamous cell carcinoma. Nat Med 23(10):1167\u20131175. https:\/\/doi.org\/10.1038\/nm.4401","journal-title":"Nat Med"},{"issue":"6","key":"869_CR11","doi-asserted-by":"publisher","first-page":"2098","DOI":"10.1093\/bib\/bby071","volume":"20","author":"W Zhang","year":"2019","unstructured":"Zhang W, Zhang H, Yang H et al (2019) Computational resources associating diseases with genotypes, phenotypes and exposures. Brief Bioinform 20(6):2098\u20132115. https:\/\/doi.org\/10.1093\/bib\/bby071","journal-title":"Brief Bioinform"},{"issue":"W1","key":"869_CR12","doi-asserted-by":"publisher","first-page":"W114","DOI":"10.1093\/nar\/gkz330","volume":"47","author":"D Hombach","year":"2019","unstructured":"Hombach D, Schuelke M, Knierim E et al (2019) MutationDistiller: user-driven identification of pathogenic DNA variants. Nucleic Acids Res 47(W1):W114\u2013W120. https:\/\/doi.org\/10.1093\/nar\/gkz330","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"869_CR13","doi-asserted-by":"publisher","first-page":"s595","DOI":"10.1016\/j.ajhg.2016.07.005","volume":"99","author":"D Smedley","year":"2016","unstructured":"Smedley D, Schubach M, Jacobsen JOB et al (2016) A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am J Hum Genet 99(3):s595\u2013606. https:\/\/doi.org\/10.1016\/j.ajhg.2016.07.005","journal-title":"Am J Hum Genet"},{"issue":"D1","key":"869_CR14","doi-asserted-by":"publisher","first-page":"D886","DOI":"10.1093\/nar\/gky1016","volume":"47","author":"P Rentzsch","year":"2019","unstructured":"Rentzsch P, Witten D, Cooper GM et al (2019) CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 47(D1):D886\u2013D894. https:\/\/doi.org\/10.1093\/nar\/gky1016","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"869_CR15","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1038\/nmeth.2832","volume":"11","author":"GR Ritchie","year":"2014","unstructured":"Ritchie GR, Dunham I, Zeggini E et al (2014) Functional annotation of noncoding sequence variants. Nat Methods 11(3):294\u2013296. https:\/\/doi.org\/10.1038\/nmeth.2832","journal-title":"Nat Methods"},{"issue":"2","key":"869_CR16","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1038\/ng.3477","volume":"48","author":"I Ionita-Laza","year":"2016","unstructured":"Ionita-Laza I, McCallum K, Xu B et al (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48(2):214\u2013220. https:\/\/doi.org\/10.1038\/ng.3477","journal-title":"Nat Genet"},{"issue":"5","key":"869_CR17","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1093\/bioinformatics\/btu703","volume":"31","author":"D Quang","year":"2015","unstructured":"Quang D, Chen Y, Xie X (2015) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31(5):761\u2013763. https:\/\/doi.org\/10.1093\/bioinformatics\/btu703","journal-title":"Bioinformatics"},{"issue":"3","key":"869_CR18","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1093\/bioinformatics\/btx536","volume":"34","author":"MF Rogers","year":"2018","unstructured":"Rogers MF, Shihab HA, Mort M et al (2018) FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 34(3):511\u2013513. https:\/\/doi.org\/10.1093\/bioinformatics\/btx536","journal-title":"Bioinformatics"},{"issue":"5","key":"869_CR19","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1002\/humu.22766","volume":"36","author":"MJ Li","year":"2015","unstructured":"Li MJ, Deng J, Wang P et al (2015) wKGGSeq: a comprehensive strategy-based and disease-targeted online framework to facilitate exome sequencing studies of inherited disorders. Hum Mutat 36(5):496\u2013503. https:\/\/doi.org\/10.1002\/humu.22766","journal-title":"Hum Mutat"},{"issue":"7","key":"869_CR20","doi-asserted-by":"publisher","first-page":"e53","DOI":"10.1093\/nar\/gkr1257","volume":"40","author":"MX Li","year":"2012","unstructured":"Li MX, Gui HS, Kwan JS et al (2012) A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases. Nucleic Acids Res 40(7):e53. https:\/\/doi.org\/10.1093\/nar\/gkr1257","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"869_CR21","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1038\/ng.3810","volume":"49","author":"YF Huang","year":"2017","unstructured":"Huang YF, Gulko B, Siepel A (2017) Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat Genet 49(4):618\u2013624. https:\/\/doi.org\/10.1038\/ng.3810","journal-title":"Nat Genet"},{"issue":"6","key":"869_CR22","doi-asserted-by":"publisher","first-page":"bbab189","DOI":"10.1093\/bib\/bbab189","volume":"22","author":"Y Wang","year":"2021","unstructured":"Wang Y, Jiang Y, Yao B et al (2021) WEVar: a novel statistical learning framework for predicting noncoding regulatory variants. Brief Bioinform 22(6):bbab189. https:\/\/doi.org\/10.1093\/bib\/bbab189","journal-title":"Brief Bioinform"},{"issue":"W1","key":"869_CR23","doi-asserted-by":"publisher","first-page":"W131","DOI":"10.1093\/nar\/gkab429","volume":"49","author":"G Pei","year":"2021","unstructured":"Pei G, Hu R, Jia P et al (2021) DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner. Nucleic Acids Res 49(W1):W131\u2013W139. https:\/\/doi.org\/10.1093\/nar\/gkab429","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"869_CR24","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.gpb.2021.08.011","volume":"21","author":"H Lu","year":"2023","unstructured":"Lu H, Ma L, Quan C et al (2023) RegVar: tissue-specific prioritization of non-coding regulatory variants. Genomics Proteom Bioinf 21(2):385\u2013395. https:\/\/doi.org\/10.1016\/j.gpb.2021.08.011","journal-title":"Genomics Proteom Bioinf"},{"issue":"24","key":"869_CR25","doi-asserted-by":"publisher","first-page":"5340","DOI":"10.1093\/bioinformatics\/btac696","volume":"38","author":"Y Wang","year":"2022","unstructured":"Wang Y, Chen L (2022) DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome. Bioinformatics 38(24):5340\u20135351. https:\/\/doi.org\/10.1093\/bioinformatics\/btac696","journal-title":"Bioinformatics"},{"issue":"12","key":"869_CR26","doi-asserted-by":"publisher","first-page":"3164","DOI":"10.1093\/bioinformatics\/btac214","volume":"38","author":"L Chen","year":"2022","unstructured":"Chen L, Wang Y, Zhao F (2022) Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence. Bioinformatics 38(12):3164\u20133172. https:\/\/doi.org\/10.1093\/bioinformatics\/btac214","journal-title":"Bioinformatics"},{"issue":"4","key":"869_CR27","doi-asserted-by":"publisher","first-page":"bbae248","DOI":"10.1093\/bib\/bbae248","volume":"25","author":"G Li","year":"2024","unstructured":"Li G, Wu J, Wang X (2024) Predicting functional UTR variants by integrating region-specific features. Brief Bioinform 25(4):bbae248. https:\/\/doi.org\/10.1093\/bib\/bbae248","journal-title":"Brief Bioinform"},{"key":"869_CR28","doi-asserted-by":"publisher","first-page":"e05005","DOI":"10.7554\/eLife.05005","volume":"4","author":"V Agarwal","year":"2015","unstructured":"Agarwal V, Bell GW, Nam JW et al (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:e05005. https:\/\/doi.org\/10.7554\/eLife.05005","journal-title":"Elife"},{"issue":"8","key":"869_CR29","doi-asserted-by":"publisher","first-page":"R90","DOI":"10.1186\/gb-2010-11-8-r90","volume":"11","author":"D Betel","year":"2010","unstructured":"Betel D, Koppal A, Agius P et al (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11(8):R90. https:\/\/doi.org\/10.1186\/gb-2010-11-8-r90","journal-title":"Genome Biol"},{"issue":"11","key":"869_CR30","doi-asserted-by":"publisher","first-page":"btad696","DOI":"10.1093\/bioinformatics\/btad696","volume":"39","author":"Y Varenyk","year":"2023","unstructured":"Varenyk Y, Spicher T, Hofacker IL et al (2023) Modified RNAs and predictions with the ViennaRNA Package. Bioinformatics 39(11):btad696. https:\/\/doi.org\/10.1093\/bioinformatics\/btad696","journal-title":"Bioinformatics"},{"issue":"14","key":"869_CR31","doi-asserted-by":"publisher","first-page":"e90","DOI":"10.1093\/bioinformatics\/btl246","volume":"22","author":"CB Do","year":"2006","unstructured":"Do CB, Woods DA, Batzoglou S (2006) CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22(14):e90\u201398. https:\/\/doi.org\/10.1093\/bioinformatics\/btl246","journal-title":"Bioinformatics"},{"issue":"10","key":"869_CR32","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1038\/s41592-022-01605-0","volume":"19","author":"HK Wayment-Steele","year":"2022","unstructured":"Wayment-Steele HK, Kladwang W, Strom AI et al (2022) RNA secondary structure packages evaluated and improved by high-throughput experiments. Nat Methods 19(10):1234\u20131242. https:\/\/doi.org\/10.1038\/s41592-022-01605-0","journal-title":"Nat Methods"},{"issue":"8","key":"869_CR33","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1101\/gr.3715005","volume":"15","author":"A Siepel","year":"2005","unstructured":"Siepel A, Bejerano G, Pedersen JS et al (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 15(8):1034\u20131050. https:\/\/doi.org\/10.1101\/gr.3715005","journal-title":"Genome Res"},{"issue":"D1","key":"869_CR34","doi-asserted-by":"publisher","first-page":"D1244","DOI":"10.1093\/nar\/gkaa1037","volume":"49","author":"Y Gao","year":"2021","unstructured":"Gao Y, Li X, Shang S et al (2021) LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements. Nucleic Acids Res 49(D1):D1244\u2013D1250. https:\/\/doi.org\/10.1093\/nar\/gkaa1037","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"869_CR35","doi-asserted-by":"publisher","first-page":"D998","DOI":"10.1093\/nar\/gkae1070","volume":"53","author":"M Cerezo","year":"2025","unstructured":"Cerezo M, Sollis E, Ji Y et al (2025) The NHGRI-EBI GWAS catalog: standards for reusability, sustainability and diversity. Nucleic Acids Res 53(D1):D998\u2013D1005. https:\/\/doi.org\/10.1093\/nar\/gkae1070","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"869_CR36","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Cancer Genome Atlas Research N, Weinstein JN, Collisson EA et al (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45(10):1113\u20131120. https:\/\/doi.org\/10.1038\/ng.2764","journal-title":"Nat Genet"},{"issue":"D1","key":"869_CR37","doi-asserted-by":"publisher","first-page":"D941","DOI":"10.1093\/nar\/gky1015","volume":"47","author":"JG Tate","year":"2019","unstructured":"Tate JG, Bamford S, Jubb HC et al (2019) COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res 47(D1):D941\u2013D947. https:\/\/doi.org\/10.1093\/nar\/gky1015","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"869_CR38","doi-asserted-by":"publisher","first-page":"D966","DOI":"10.1093\/nar\/gkae1078","volume":"53","author":"JM Mudge","year":"2025","unstructured":"Mudge JM, Carbonell-Sala S, Diekhans M et al (2025) GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Res 53(D1):D966\u2013D975. https:\/\/doi.org\/10.1093\/nar\/gkae1078","journal-title":"Nucleic Acids Res"},{"issue":"18","key":"869_CR39","doi-asserted-by":"publisher","first-page":"3426","DOI":"10.1016\/j.cell.2022.08.004","volume":"185","author":"M Byrska-Bishop","year":"2022","unstructured":"Byrska-Bishop M, Evani US, Zhao X et al (2022) High-coverage whole-genome sequencing of the expanded 1000 genomes project cohort including 602 trios. Cell 185(18):3426\u20133440. https:\/\/doi.org\/10.1016\/j.cell.2022.08.004","journal-title":"Cell"},{"issue":"10","key":"869_CR40","doi-asserted-by":"publisher","first-page":"btae578","DOI":"10.1093\/bioinformatics\/btae578","volume":"40","author":"W Jin","year":"2024","unstructured":"Jin W, Xia Y, Nizomov J et al (2024) MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants. Bioinformatics 40(10):btae578. https:\/\/doi.org\/10.1093\/bioinformatics\/btae578","journal-title":"Bioinformatics"},{"issue":"D1","key":"869_CR41","doi-asserted-by":"publisher","first-page":"D155","DOI":"10.1093\/nar\/gky1141","volume":"47","author":"A Kozomara","year":"2019","unstructured":"Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Res 47(D1):D155\u2013D162. https:\/\/doi.org\/10.1093\/nar\/gky1141","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"869_CR42","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1109\/TPAMI.2016.2599174","volume":"39","author":"J Donahue","year":"2017","unstructured":"Donahue J, Hendricks LA, Rohrbach M et al (2017) Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans Pattern Anal Mach Intell 39(4):677\u2013691. https:\/\/doi.org\/10.1109\/TPAMI.2016.2599174","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"869_CR43","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1137\/0515056","volume":"15","author":"AGJ Morlet","year":"1984","unstructured":"Morlet AGJ (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15(4):723\u2013736. https:\/\/doi.org\/10.1137\/0515056","journal-title":"SIAM J Math Anal"},{"key":"869_CR44","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","journal-title":"Adv Neural Inf Process Syst"},{"key":"869_CR45","doi-asserted-by":"publisher","first-page":"113473","DOI":"10.1016\/j.knosys.2025.113473","volume":"318","author":"H Zhu","year":"2025","unstructured":"Zhu H, Wang G, Liao Z et al (2025) AITL-Net: An adaptive interpretable transfer learning network with robust generalization for liver cancer recognition. Knowl-Based Syst\u200c 318:113473. https:\/\/doi.org\/10.1016\/j.knosys.2025.113473","journal-title":"KNOWL-BASED SYST\u200c"},{"key":"869_CR46","doi-asserted-by":"publisher","unstructured":"Liang H, Ouyang Z, Zeng Y et al (2020) Training interpretable convolutional neural networks by differentiating class-specific filters. In: 16th European Conference on Computer Vision (ECCV), pp 622\u2013638. https:\/\/doi.org\/10.48550\/arXiv.2007.08194","DOI":"10.48550\/arXiv.2007.08194"},{"key":"869_CR47","doi-asserted-by":"publisher","unstructured":"Therneau TM, Grambsch PM (2000) The cox model. In: Modeling survival data: extending the Cox model, Springer, pp 39\u201377. https:\/\/link.springer.com\/chapter\/https:\/\/doi.org\/10.1007\/978-1-4757-3294-8_3","DOI":"10.1007\/978-1-4757-3294-8_3"},{"issue":"1","key":"869_CR48","doi-asserted-by":"publisher","first-page":"e70013","DOI":"10.1049\/syb2.70013","volume":"19","author":"G Huang","year":"2025","unstructured":"Huang G, Xue T, Chen W et al (2025) SVM-LncRNAPro: an SVM-based method for predicting long noncoding RNA promoters. IET Syst Biol 19(1):e70013. https:\/\/doi.org\/10.1049\/syb2.70013","journal-title":"IET Syst Biol"},{"key":"869_CR49","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.ymeth.2025.01.013","volume":"234","author":"S Wang","year":"2025","unstructured":"Wang S, Yu ZG, Han GS (2025) MVSLLnc: LncRNA subcellular localization prediction based on multi-source features and two-stage voting strategy. Methods 234:324\u2013332. https:\/\/doi.org\/10.1016\/j.ymeth.2025.01.013","journal-title":"Methods"},{"key":"869_CR50","doi-asserted-by":"publisher","first-page":"108336","DOI":"10.1016\/j.compbiolchem.2024.108336","volume":"115","author":"L Zhang","year":"2025","unstructured":"Zhang L, Gao S, Yuan Q et al (2025) An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations. Comput Biol Chem 115:108336. https:\/\/doi.org\/10.1016\/j.compbiolchem.2024.108336","journal-title":"Comput Biol Chem"},{"issue":"6","key":"869_CR51","doi-asserted-by":"publisher","first-page":"168609","DOI":"10.1016\/j.jmb.2024.168609","volume":"437","author":"Q Chen","year":"2025","unstructured":"Chen Q, Qiu J, Lan W et al (2025) Similarity-guided graph contrastive learning for lncRNA-disease association prediction. J Mol Biol 437(6):168609. https:\/\/doi.org\/10.1016\/j.jmb.2024.168609","journal-title":"J Mol Biol"},{"issue":"1","key":"869_CR52","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s12859-023-05191-6","volume":"24","author":"K Chen","year":"2023","unstructured":"Chen K, Zhu X, Wang J et al (2023) ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction. BMC Bioinformatics 24(1):68. https:\/\/doi.org\/10.1186\/s12859-023-05191-6","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"869_CR53","doi-asserted-by":"publisher","first-page":"e100","DOI":"10.1002\/qub2.100","volume":"13","author":"YPX Cheng","year":"2025","unstructured":"Cheng YPX, Yang Y (2025) Loc4Lnc: Accurate prediction of long noncoding RNA subcellular localization via enhanced RNA sequence representation. Quant Biology 13(3):e100. https:\/\/doi.org\/10.1002\/qub2.100","journal-title":"Quant Biology"},{"issue":"1","key":"869_CR54","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12915-025-02148-4","volume":"23","author":"W Hu","year":"2025","unstructured":"Hu W, Yue Y, Yan R et al (2025) An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy. BMC Biol 23(1):47. https:\/\/doi.org\/10.1186\/s12915-025-02148-4","journal-title":"BMC Biol"},{"key":"869_CR55","doi-asserted-by":"publisher","first-page":"141538","DOI":"10.1016\/j.ijbiomac.2025.141538","volume":"306","author":"G Huang","year":"2025","unstructured":"Huang G, Lyu J, Dai Q et al (2025) EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs. Int J Biol Macromol 306:141538. https:\/\/doi.org\/10.1016\/j.ijbiomac.2025.141538","journal-title":"Int J Biol Macromol"},{"key":"869_CR56","doi-asserted-by":"publisher","first-page":"152521","DOI":"10.1016\/j.bbrc.2025.152521","volume":"781","author":"B Huang","year":"2025","unstructured":"Huang B, Luo Y, Zhuang Y et al (2025) LncMamba: A deep learning model for LncRNA localization prediction based on the Mamba model. Biochem Biophys Res Commun 781:152521. https:\/\/doi.org\/10.1016\/j.bbrc.2025.152521","journal-title":"Biochem Biophys Res Commun"},{"issue":"1","key":"869_CR57","doi-asserted-by":"publisher","first-page":"102425","DOI":"10.1016\/j.omtn.2024.102425","volume":"36","author":"J Li","year":"2025","unstructured":"Li J, Ju Y, Zou Q et al (2025) lncRNA localization and feature interpretability analysis. Mol Ther Nucleic Acids 36(1):102425. https:\/\/doi.org\/10.1016\/j.omtn.2024.102425","journal-title":"Mol Ther Nucleic Acids"},{"issue":"1","key":"869_CR58","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1186\/s12864-024-11168-3","volume":"25","author":"L Tan","year":"2024","unstructured":"Tan L, Mengshan L, Yu F et al (2024) Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding. BMC Genomics 25(1):1253. https:\/\/doi.org\/10.1186\/s12864-024-11168-3","journal-title":"BMC Genomics"},{"key":"869_CR59","doi-asserted-by":"publisher","unstructured":"Liu Z, Bai T, Jiang D et al (2024) MVLncStack: A novel method for lncRNA subcellular localization prediction based on multi-view features. In: 2024 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 1015\u20131020. https:\/\/doi.org\/10.1109\/BIBM62325.2024.10821942","DOI":"10.1109\/BIBM62325.2024.10821942"},{"issue":"3","key":"869_CR60","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s12539-024-00616-z","volume":"16","author":"L Liu","year":"2024","unstructured":"Liu L, Wei Y, Tan Z et al (2024) Predicting circRNA-RBP binding sites using a hybrid deep neural network. Interdiscip Sci 16(3):635\u2013648. https:\/\/doi.org\/10.1007\/s12539-024-00616-z","journal-title":"Interdiscip Sci"},{"issue":"2","key":"869_CR61","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1007\/s12539-024-00619-w","volume":"16","author":"L Peng","year":"2024","unstructured":"Peng L, Ren M, Huang L et al (2024) GEnDDn: an lncRNA-disease association identification framework based on dual-net neural architecture and deep neural network. Interdiscip Sci 16(2):418\u2013438. https:\/\/doi.org\/10.1007\/s12539-024-00619-w","journal-title":"Interdiscip Sci"},{"key":"869_CR62","doi-asserted-by":"publisher","DOI":"10.1101\/2025.01.09.632168","author":"L Romeijn","year":"2025","unstructured":"Romeijn L, Cats D, Wolstencroft K et al (2025) LncRNA-BERT: an RNA language model for classifying coding and long non-coding RNA. bioRxiv. https:\/\/doi.org\/10.1101\/2025.01.09.632168","journal-title":"bioRxiv"},{"issue":"3","key":"869_CR63","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.jobcr.2025.02.012","volume":"15","author":"PK Yadalam","year":"2025","unstructured":"Yadalam PK, Ardila CM (2025) Enhanced hierarchical attention networks for predictive interactome analysis of LncRNA and CircRNA in oral herpes virus. J Oral Biol Craniofac Res 15(3):445\u2013453. https:\/\/doi.org\/10.1016\/j.jobcr.2025.02.012","journal-title":"J Oral Biol Craniofac Res"},{"issue":"1","key":"869_CR64","doi-asserted-by":"publisher","first-page":"bbaf046","DOI":"10.1093\/bib\/bbaf046","volume":"26","author":"T Yang","year":"2024","unstructured":"Yang T, He Y, Wang Y (2024) Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences. Brief Bioinform 26(1):bbaf046. https:\/\/doi.org\/10.1093\/bib\/bbaf046","journal-title":"Brief Bioinform"},{"issue":"1","key":"869_CR65","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12859-023-05625-1","volume":"25","author":"D Yao","year":"2024","unstructured":"Yao D, Li B, Zhan X et al (2024) GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations. BMC Bioinformatics 25(1):5. https:\/\/doi.org\/10.1186\/s12859-023-05625-1","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"869_CR66","doi-asserted-by":"publisher","first-page":"bbad466","DOI":"10.1093\/bib\/bbad466","volume":"25","author":"L Peng","year":"2023","unstructured":"Peng L, Huang L, Su Q et al (2023) LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine. Brief Bioinform 25(1):bbad466. https:\/\/doi.org\/10.1093\/bib\/bbad466","journal-title":"Brief Bioinform"},{"key":"869_CR67","doi-asserted-by":"publisher","unstructured":"Wojciech Zaremba IS, Oriol V (2014) Recurrent neural network regularization. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1409.2329","DOI":"10.48550\/arXiv.1409.2329"},{"key":"869_CR68","doi-asserted-by":"publisher","unstructured":"Cho K, Van Merri\u00ebnboer B, Gul\u00e7ehre \u00c7 et al (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. https:\/\/doi.org\/10.48550\/arXiv.1406.1078. arXiv","DOI":"10.48550\/arXiv.1406.1078"}],"container-title":["Interdisciplinary Sciences: Computational Life Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-026-00869-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12539-026-00869-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-026-00869-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T06:39:44Z","timestamp":1787726384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12539-026-00869-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,26]]},"references-count":68,"alternative-id":["869"],"URL":"https:\/\/doi.org\/10.1007\/s12539-026-00869-w","relation":{},"ISSN":["1913-2751","1867-1462"],"issn-type":[{"value":"1913-2751","type":"print"},{"value":"1867-1462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,26]]},"assertion":[{"value":"2 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have declared no conflicts of interest.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}}]}}